class_docstr: Plot ROC curve
  
  Parameters
  ----------
  fpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing false positive rates such that element i is the false
      positive rate of predictions with score >= `thresholds[i]`.
  
  tpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing true positive rates such that element `i` is the true
      positive rate of predictions with score >= `thresholds[i]`.
  
  label : list of str, default: None
      Set curve labels
  
  ax: matplotlib Axes, default: None
      Axes object to draw the plot onto, otherwise uses current Axes
  
  Examples
  --------
  .. plot:: ../examples/roc_binary_classification.py
  
  .. plot:: ../examples/roc_comparison.py
  
  .. plot:: ../examples/roc_multi_classification.py
  
  Notes
  -----
  .. versionadded:: 0.8.4
code: def plot(self, ax=None):
      if ax is None:
          _, ax = plt.subplots()
  
      _generate_plot_from_fpr_tpr_lists(
          self.fpr, self.tpr, ax, label=self.label
      )
  
      self.ax = ax
      self.figure_ = ax.figure
  
      return self
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from sklearn.preprocessing import label_binarize
  from ..telemetry import SKLearnEvaluationLogger
  from ..util import is_column_vector, is_row_vector
  from sklearn_evaluation import __version__
  import json
  from pathlib import Path
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 5 for calling the ROC obj's underlying member method code with example usage: obj.plot(ax) and returns: self